Skin cancer screening may involve the evaluation of features on the surface of the skin such as moles, growths, and/or lesions over time. The assessment of changes to the skin features may include identifying the presence of new features, the absence of old features, and/or changes to the physical characteristics of existing features. These changes may indicate that the skin features are cancerous. Identifying changes to the skin surface features may allow physicians to detect cancerous growths before the development of metastatic disease.
Skin cancer screening is a time consuming process for physicians. The skin must be examined closely and the physician may record descriptions such as the location, color, shape, and/or size of suspect features on the skin. A patient will often return for a second physician evaluation several months later to determine whether the suspect features have changed over time. Comparing written notes describing skin features at a previous time has a limited utility to a physician. For example, it may be difficult to accurately determine a potential change in skin features based on such written descriptions.
The development of automated skin imaging technologies has similar problems with reliability. Traditional imaging systems use affine image registration algorithms to detect anomalies in skin composition. To detect anomalies, these imaging systems rely on the linear alignment of images of the skin that are taken at different times. Misalignment of the images is prevalent in traditional imaging systems due to the nonlinear nature of patient skin composition. For example, a patient may gain or lose a significant amount of weight during the several months in between physician appointments, causing inconsistencies in the characteristics of the skin surface area over time.
The linear nature of affine image registration leads to a lack of operational fidelity. Affine image registration causes misdiagnosis including false-positives in which features are erroneously determined to be new skin features. Misdiagnosis also occurs as false-negatives in which pre-existing skin features are erroneously declared absent. The high rate of misdiagnosis in the form of false-positives and false-negatives requires a physician or other user of the imaging system to manually re-evaluate each anomalous skin feature that is identified by the imaging system. This effect defeats the purpose of automating a skin imaging system and prevents the system from being a viable and clinically useful tool for physicians.